Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information
Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Yan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
Abnormal condition identification condition-relevant information |
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| Anmerkung: |
© ICROS, KIEE and Springer 2024 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of control, automation, and systems - Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009, 22(2024), 3 vom: 18. Jan., Seite 851-866 |
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| Übergeordnetes Werk: |
volume:22 ; year:2024 ; number:3 ; day:18 ; month:01 ; pages:851-866 |
| Links: |
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| DOI / URN: |
10.1007/s12555-022-1105-5 |
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| Katalog-ID: |
SPR055119395 |
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| 245 | 1 | 0 | |a Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information |
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| 520 | |a Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. | ||
| 650 | 4 | |a Abnormal condition identification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a condition-relevant information |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a electro-fused magnesia smelting process |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a feature fusion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a multi-source heterogeneous information |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Zhenyu |4 aut | |
| 700 | 1 | |a Wang, Fuli |4 aut | |
| 700 | 1 | |a Xiong, Yulu |4 aut | |
| 700 | 1 | |a Ma, Ruicheng |4 aut | |
| 700 | 1 | |a Chu, Fei |4 aut | |
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10.1007/s12555-022-1105-5 doi (DE-627)SPR055119395 (SPR)s12555-022-1105-5-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ 50.20 bkl 50.23 bkl Liu, Yan verfasserin (orcid)0000-0002-8003-8222 aut Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2024 Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. Abnormal condition identification (dpeaa)DE-He213 condition-relevant information (dpeaa)DE-He213 electro-fused magnesia smelting process (dpeaa)DE-He213 feature fusion (dpeaa)DE-He213 multi-source heterogeneous information (dpeaa)DE-He213 Liu, Zhenyu aut Wang, Fuli aut Xiong, Yulu aut Ma, Ruicheng aut Chu, Fei aut Enthalten in International journal of control, automation, and systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 22(2024), 3 vom: 18. Jan., Seite 851-866 Online-Ressource (DE-627)59477926X (DE-600)2486545-X (DE-576)307016277 2005-4092 nnns volume:22 year:2024 number:3 day:18 month:01 pages:851-866 https://dx.doi.org/10.1007/s12555-022-1105-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.20 Automatisierungstechnik VZ 50.23 Regelungstechnik Steuerungstechnik VZ AR 22 2024 3 18 01 851-866 |
| spelling |
10.1007/s12555-022-1105-5 doi (DE-627)SPR055119395 (SPR)s12555-022-1105-5-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ 50.20 bkl 50.23 bkl Liu, Yan verfasserin (orcid)0000-0002-8003-8222 aut Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2024 Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. Abnormal condition identification (dpeaa)DE-He213 condition-relevant information (dpeaa)DE-He213 electro-fused magnesia smelting process (dpeaa)DE-He213 feature fusion (dpeaa)DE-He213 multi-source heterogeneous information (dpeaa)DE-He213 Liu, Zhenyu aut Wang, Fuli aut Xiong, Yulu aut Ma, Ruicheng aut Chu, Fei aut Enthalten in International journal of control, automation, and systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 22(2024), 3 vom: 18. Jan., Seite 851-866 Online-Ressource (DE-627)59477926X (DE-600)2486545-X (DE-576)307016277 2005-4092 nnns volume:22 year:2024 number:3 day:18 month:01 pages:851-866 https://dx.doi.org/10.1007/s12555-022-1105-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.20 Automatisierungstechnik VZ 50.23 Regelungstechnik Steuerungstechnik VZ AR 22 2024 3 18 01 851-866 |
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10.1007/s12555-022-1105-5 doi (DE-627)SPR055119395 (SPR)s12555-022-1105-5-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ 50.20 bkl 50.23 bkl Liu, Yan verfasserin (orcid)0000-0002-8003-8222 aut Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2024 Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. Abnormal condition identification (dpeaa)DE-He213 condition-relevant information (dpeaa)DE-He213 electro-fused magnesia smelting process (dpeaa)DE-He213 feature fusion (dpeaa)DE-He213 multi-source heterogeneous information (dpeaa)DE-He213 Liu, Zhenyu aut Wang, Fuli aut Xiong, Yulu aut Ma, Ruicheng aut Chu, Fei aut Enthalten in International journal of control, automation, and systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 22(2024), 3 vom: 18. Jan., Seite 851-866 Online-Ressource (DE-627)59477926X (DE-600)2486545-X (DE-576)307016277 2005-4092 nnns volume:22 year:2024 number:3 day:18 month:01 pages:851-866 https://dx.doi.org/10.1007/s12555-022-1105-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.20 Automatisierungstechnik VZ 50.23 Regelungstechnik Steuerungstechnik VZ AR 22 2024 3 18 01 851-866 |
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10.1007/s12555-022-1105-5 doi (DE-627)SPR055119395 (SPR)s12555-022-1105-5-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ 50.20 bkl 50.23 bkl Liu, Yan verfasserin (orcid)0000-0002-8003-8222 aut Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2024 Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. Abnormal condition identification (dpeaa)DE-He213 condition-relevant information (dpeaa)DE-He213 electro-fused magnesia smelting process (dpeaa)DE-He213 feature fusion (dpeaa)DE-He213 multi-source heterogeneous information (dpeaa)DE-He213 Liu, Zhenyu aut Wang, Fuli aut Xiong, Yulu aut Ma, Ruicheng aut Chu, Fei aut Enthalten in International journal of control, automation, and systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 22(2024), 3 vom: 18. Jan., Seite 851-866 Online-Ressource (DE-627)59477926X (DE-600)2486545-X (DE-576)307016277 2005-4092 nnns volume:22 year:2024 number:3 day:18 month:01 pages:851-866 https://dx.doi.org/10.1007/s12555-022-1105-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.20 Automatisierungstechnik VZ 50.23 Regelungstechnik Steuerungstechnik VZ AR 22 2024 3 18 01 851-866 |
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10.1007/s12555-022-1105-5 doi (DE-627)SPR055119395 (SPR)s12555-022-1105-5-e DE-627 ger DE-627 rakwb eng 620 VZ 620 VZ 50.20 bkl 50.23 bkl Liu, Yan verfasserin (orcid)0000-0002-8003-8222 aut Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2024 Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. Abnormal condition identification (dpeaa)DE-He213 condition-relevant information (dpeaa)DE-He213 electro-fused magnesia smelting process (dpeaa)DE-He213 feature fusion (dpeaa)DE-He213 multi-source heterogeneous information (dpeaa)DE-He213 Liu, Zhenyu aut Wang, Fuli aut Xiong, Yulu aut Ma, Ruicheng aut Chu, Fei aut Enthalten in International journal of control, automation, and systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 22(2024), 3 vom: 18. Jan., Seite 851-866 Online-Ressource (DE-627)59477926X (DE-600)2486545-X (DE-576)307016277 2005-4092 nnns volume:22 year:2024 number:3 day:18 month:01 pages:851-866 https://dx.doi.org/10.1007/s12555-022-1105-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.20 Automatisierungstechnik VZ 50.23 Regelungstechnik Steuerungstechnik VZ AR 22 2024 3 18 01 851-866 |
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Enthalten in International journal of control, automation, and systems 22(2024), 3 vom: 18. Jan., Seite 851-866 volume:22 year:2024 number:3 day:18 month:01 pages:851-866 |
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Liu, Yan @@aut@@ Liu, Zhenyu @@aut@@ Wang, Fuli @@aut@@ Xiong, Yulu @@aut@@ Ma, Ruicheng @@aut@@ Chu, Fei @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR055119395</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240314064710.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240314s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12555-022-1105-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR055119395</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12555-022-1105-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.23</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-8003-8222</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© ICROS, KIEE and Springer 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. 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Liu, Yan |
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Liu, Yan ddc 620 bkl 50.20 bkl 50.23 misc Abnormal condition identification misc condition-relevant information misc electro-fused magnesia smelting process misc feature fusion misc multi-source heterogeneous information Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information |
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620 VZ 50.20 bkl 50.23 bkl Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information Abnormal condition identification (dpeaa)DE-He213 condition-relevant information (dpeaa)DE-He213 electro-fused magnesia smelting process (dpeaa)DE-He213 feature fusion (dpeaa)DE-He213 multi-source heterogeneous information (dpeaa)DE-He213 |
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abnormal condition identification for the electro-fused magnesia smelting process based on condition-relevant information |
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Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information |
| abstract |
Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. © ICROS, KIEE and Springer 2024 |
| abstractGer |
Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. © ICROS, KIEE and Springer 2024 |
| abstract_unstemmed |
Abstract To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevant information (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, the features related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domain knowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormal condition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on the fused feature. They are further integrated according to the frequency of each condition in the actual production process to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness and feasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process. © ICROS, KIEE and Springer 2024 |
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3 |
| title_short |
Abnormal Condition Identification for the Electro-fused Magnesia Smelting Process Based on Condition-relevant Information |
| url |
https://dx.doi.org/10.1007/s12555-022-1105-5 |
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| author2 |
Liu, Zhenyu Wang, Fuli Xiong, Yulu Ma, Ruicheng Chu, Fei |
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Liu, Zhenyu Wang, Fuli Xiong, Yulu Ma, Ruicheng Chu, Fei |
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| doi_str |
10.1007/s12555-022-1105-5 |
| up_date |
2024-07-03T13:30:06.219Z |
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| score |
7.399544 |